A hybrid estimation of distribution algorithm for simulation-based scheduling in a stochastic permutation flowshop. (December 2015)

• Record Type: Journal Article
• Authors: Wang, K.; Choi, S.H.; Lu, H.
• Is Part Of: Computers & industrial engineering. Volume 90(2015)
• Page Start: 186
• Page End: 196
• Publication Date: 2015-12
• Subjects: Permutation flowshop scheduling -- Stochastic processing times -- Estimation of distribution algorithm -- Genetic algorithm -- Meta-model; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2015.09.007
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: We hybridise EDA with GA to solve stochastic permutation flowshop scheduling problems. An efficient two-stage simulation model is developed for performance evaluation. A self-adaptive learning mechanism is adopted to generate the population. Abstract: The permutation flowshop scheduling problem (PFSP) is NP-complete and tends to be more complicated when considering stochastic uncertainties in the real-world manufacturing environments. In this paper, a two-stage simulation-based hybrid estimation of distribution algorithm (TSSB-HEDA) is presented to schedule the permutation flowshop under stochastic processing times. To deal with processing time uncertainty, TSSB-HEDA evaluates candidate solutions using a novel two-stage simulation model (TSSM). This model first adopts the regression-based meta-modelling technique to determine a number of promising candidate solutions with less computation cost, and then uses a more accurate but time-consuming simulator to evaluate the performance of these selected ones. In addition, to avoid getting trapped into premature convergence, TSSB-HEDA employs both the probabilistic model of EDA and genetic operators of genetic algorithm (GA) to generate the offspring individuals. Enlightened by the weight training process of neural networks, a self-adaptive learning mechanism (SALM) is employed to dynamically adjust the ratio of offspring individuals generated by the probabilistic model. Computational experiments on Taillard's benchmarks show that TSSB-HEDA is competitive in terms of both solution quality and computational performance.